MATLAB Implementation of LMD Algorithm for Signal Decomposition

In the field of signal processing, the Local Mean Decomposition (LMD) algorithm serves as a fundamental method for signal analysis. This implementation allows users to decompose signals into multiple components with customizable decomposition levels, enabling extraction of different-scale signal characteristics. The algorithm operates by iteratively extracting product functions (PFs) through a sifting process that separates local mean and envelope functions. Key implementation aspects include: - Configurable decomposition level parameter for controlling analysis depth - Visualization of decomposed waveforms for result interpretation - Efficient computation through MATLAB's vectorized operations and signal processing toolbox functions - Automated stopping criteria based on energy ratio or iteration counts The LMD method demonstrates excellent operational performance, achieving efficient signal processing through its adaptive decomposition approach that handles non-stationary signals effectively. The code structure typically involves main functions for envelope estimation, local mean calculation, and PF extraction loops, providing researchers with a practical tool for time-frequency analysis applications.